Transactional-unstructured data driven sequential federated query method for distributed systems

ABSTRACT

Methods and systems are disclosed for receiving a sequential federated query; deconstructing the sequential federated query into query elements; identifying a Transactional-Unstructured Data Source (TUDS), a Contextual-Characteristic Data Source (CCDS), and a data organization parameter based on the query elements; generating a TUDS result data set from the TUDS based on the parameter; processing the TUDS result data set and the parameter to develop a CCDS query; generating a CCDS result data set from the CCDS based on the developed CCDS query and the parameter; generating a final sequential federated query data set based on the CCDS result data set and the parameters; processing a formatted sequential federated query data set based on the processing of the final sequential federated query data set and the parameter; and providing the formatted sequential federated query data set to a management system for action.

RELATED APPLICATIONS

This application is a U.S. National Stage Application under 35 U.S.C. §371 of International Application No. PCT/US2017/039028, filed Jun. 23,2017, titled TRANSACTIONAL-UNSTRUCTURED DATA DRIVEN SEQUENTIAL FEDERATEDQUERY METHOD FOR DISTRIBUTED SYSTEMS, which claims the benefit under 35U.S.C. § 119(e) of U.S. Provisional Application No. 62/354,039, filedJun. 23, 2016, titled TRANSACTIONAL-UNSTRUCTURED DATA DRIVEN SEQUENTIALFEDERATED QUERY METHODS FOR DISTRIBUTED SYSTEMS, both of which arehereby incorporated by reference in their entirety for all purposes.

FIELD OF THE INVENTION

Embodiments of the present disclosure relate generally to methods ofdata organization, and more specifically to systems and methods forquerying data associated with distributed residential, commercial,and/or industrial systems.

PRIORITY CLAIM

This application claims priority to and benefit from the followingprovisional patent application: U.S. Provisional Application Ser. No.U.S. 62/354,039 titled “Transactional-Unstructured Data DrivenSequential Federated Query Methods for Distributed Systems” filed onJun. 23, 2016. The entire contents of the aforementioned patentapplication are expressly incorporated by reference herein.

BACKGROUND Description of the Related Art

This application claims priority to and benefit from the followingprovisional patent application: U.S. Provisional Application Ser. No.U.S. 62/354,039 titled “Transactional-Unstructured Data DrivenSequential Federated Query Methods for Distributed Systems” filed onJun. 23, 2016. The entire contents of the aforementioned patentapplications are expressly incorporated by reference herein.

The Internet of Things (IoT) promises to interconnect elements togetheron a massive scale. These connected elements may include devices,vehicles, homes, cities, and any other system or collection of systemsthat contain the applicable electronic hardware, software, sensors, andconnectivity that enables these systems to collect and exchange data.Such amalgamation allows this massive amount of data, when collected ona global scale, transform into actionable information. Interactions andcollaborations between systems form in order to fulfill one or morespecific tasks. Such tasks differ according to the context andenvironment of application. For example, tasks may range from sensingand monitoring of an environmental characteristic such as temperature orhumidity of a single room to controlling and optimization of an entirebuilding or facility in order to achieve a larger objective such as anenergy management strategy.

Depending on the application, connected elements include heterogeneousand/or homogenous hardware that facilitate sensing, operation,actuation, data capture, data storage, data processing and/or dataanalytics. Each type of element includes a unique data structure thatdetails a digital representation of the capabilities of the hardwareitself and/or measured parameters. For example, a temperature sensor mayimplement different hardware to facilitate temperature measurements.This hardware may also in turn provide different data parameters,values, and/or operational units, such as temperature measurement units,time format, MAC address, IP address, and/or CPU type data.

Data structure unit, value, and parameter complexities are exacerbatedby storage and organization distributions that may exist situated acrossany number of memory storage locations or hybrid data structures withinmultiple repositories. Further, such data accessibility is complicatedby trying to unify accessibility to data sets that span a large andinconsistent temporal period, storing periodic, state based orunstructured data. Accordingly, with truly massive amounts ofheterogeneous data available through the wide variety of availableconnected elements and their respective data structures, efficiently andeffectively analyzing this voluminous data presents a serious challenge.

SUMMARY

Methods and systems that facilitate processing and executing sequentialfederated queries for identifying and making accessible, actionable, andoperational data associated with or generated by residential,commercial, and/or industrial systems are discussed herein. Efficientand effective data processing gains are realized through a two part,sequential federated query process. In various embodiments, thesequential federated query accesses, filters, processes, translates,queries, and/or performs operations on a Transactional-Unstructured DataSource (TUDS) and subsequently a Contextual-Characteristic Data Source(CCDS). A TUDS data organization may include data such as a Time SeriesID, a time and date stamp, and/or a parameter value. Further,unstructured or multi-structured data may also be included within aTUDS. A CCDS data organization may include data such as protocols,usage, physical quantities, or topography relationships as well asontologies specific to the application such as data center, buildings,or smart grid.

Methods and systems are disclosed for executing a sequential federatedquery for information in residential, commercial, and/or distributedsystems. A method of processing a sequential federated query fordistributed systems may comprise receiving a sequential federated query;deconstructing the sequential federated query into query elements;identifying a Transactional-Unstructured Data Source (TUDS), aContextual-Characteristic Data Source (CCDS), and a data organizationparameter based on the query elements; generating a TUDS result data setfrom the TUDS based on the data organization parameter; processing theTUDS result data set and the data organization parameter to develop aCCDS query; generating a CCDS result data set from the CCDS based on thedeveloped CCDS query and the data organization parameter; generating afinal sequential federated query data set based on the CCDS result dataset and the data organization parameters; processing a formattedsequential federated query data set based on the processing of the finalsequential federated query data set and the data organization parameter;and providing the formatted sequential federated query data set to amanagement system for action.

Principles of the disclosure contemplate receiving the sequentialfederated query is initiated from at least one of a user and a system.Further, receiving the sequential federated query is from at least oneof a database, a user interface, and an application interface.

In some embodiments of the disclosure, the query elements are timeseries based. Further, the time series query elements are one of a timeseries data, time series state data, time stamp data, and unstructureddata formats.

In some embodiments of the disclosure, one of a plurality ofContextual-Characteristic Data Source (CCDS), Transactional-UnstructuredData Source (TUDS), and data organization parameters based on the queryelements are utilized. Further, the TUDS result data set includesoperational anomaly data generated by connected elements.

Further embodiments of the disclosure contemplate wherein CCDS resultset is contextual based data. Additionally, the contextual based dataare one of data locations, data operations, and data sources. Further,the management system for action is a Building Management System (BMS).

Principles of the disclosure contemplate a non-transitory computerreadable medium storing sequences of computer-executable instructionsfor processing a sequential federated query for distributed systems, thesequences of computer executable instructions including instructionsthat instruct at least one processor to, receive a sequential federatedquery, deconstruct the sequential federated query into query elements,identify a Transactional-Unstructured Data Source (TUDS), aContextual-Characteristic Data Source (CCDS), and a data organizationparameter based on the query elements, generate a TUDS result data setfrom the TUDS based on the data organization parameter, process the TUDSresult data set and the data organization parameter to develop a CCDSquery, generate a CCDS result data set from the CCDS based on thedeveloped CCDS query and the data organization parameter, generate afinal sequential federated query data set based on the CCDS result dataset and the data organization parameters, process a formatted sequentialfederated query data set based on the processing of the final sequentialfederated query data set and the data organization parameter, andprovide at the processor, the formatted sequential federated query dataset to a management system for action.

Principles of the disclosure contemplate at least one processor isfurther configured to receive the sequential federated query isinitiated from at least one of a user and a system. Further, at leastone processor is further configured from at least one of a database, auser interface, and an application interface.

In some embodiments of the disclosure, at least one processor is furtherconfigured where the query elements are time series based. Further, atleast one processor is further configured where the time series queryelements are at least one of time series data, time series state data,time stamp data, and unstructured data formats.

Further embodiments of the disclosure contemplate at least one processoris further configured to a plurality of Contextual-Characteristic DataSource (CCDS), Transactional-Unstructured Data Source (TUDS), and dataorganization parameter based on the query elements are utilized.Further, at least one processor is further configured to the TUDS resultdata set includes operational anomaly data generated by connectedelements.

Further embodiments of the disclosure contemplate at least one processoris further configured wherein CCDS result set is contextual based data.Additionally, at least one processor is further configured that thecontextual based data are one of data locations, data operations, anddata sources. Further, at least one processor is further configured thatthe management system for action is a Building Management System (BMS).

BRIEF DESCRIPTION OF THE DRAWINGS

These accompanying drawings are not intended to be drawn to scale. Inthe drawings, each identical or nearly identical component that isillustrated in various figures is represented by a line numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 illustrates aspects of a system for executing a sequentialfederated query in accordance with various embodiments of thisdisclosure;

FIG. 2 illustrates aspects of heterogeneous hardware connected elementsthat may connect to a system for executing a sequential federated queryin accordance with various embodiments of this disclosure;

FIG. 3 illustrates an exemplary deployment of heterogeneous hardwareconnected elements providing data for a system that executes asequential federated query in accordance with various embodiments ofthis disclosure;

FIG. 4A illustrates an exemplary data organization construct providingdata for a system that executes a sequential federated query inaccordance with various embodiments of this disclosure;

FIG. 4B illustrates an exemplary Transactional-Unstructured Data Source(TUDS) data organization construct providing data for a system thatexecutes a sequential federated query in accordance with variousembodiments of this disclosure;

FIG. 4C illustrates an exemplary Contextual-Characteristic Data Source(CCDS) data organization construct providing data for a system thatexecutes a sequential federated query method in accordance with variousembodiments of this disclosure;

FIG. 5 illustrates a block diagram of system components for executing asequential federated query in accordance with various embodiments ofthis disclosure;

FIGS. 6A and 6B is a flow diagram illustrating methods executing asequential federated query method in accordance with various embodimentsof this disclosure;

FIG. 7 illustrates an example system for data organization andprocessing flow diagram for executing a sequential federated query inaccordance with various embodiments of this disclosure;

FIG. 8 illustrates an exemplary system for executing a sequentialfederated query method in accordance with various embodiments of thisdisclosure;

FIG. 9 is a functional block diagram of a processing system inaccordance with embodiments of this disclosure;

FIG. 10 is a functional block diagram of a processing storage system inaccordance with the processing system of FIG. 9.

DETAILED DESCRIPTION

This disclosure is not limited in its application to the details ofconstruction and the arrangement of components set forth in thefollowing descriptions or illustrated by the drawings. The disclosure iscapable of other embodiments and of being practiced or of being carriedout in various ways. In addition, the phraseology and terminology usedherein is for the purpose of descriptions and should not be regarded aslimiting. The use of “including,” “comprising,” “having,” “involving,”and variations herein, are meant to be open-ended, i.e. “including butnot limited to.”

In the emerging world of the Internet of Things (IoT) or more generally,Cyber Physical Systems (CPS), a convergence of multiple technologies isunderway that facilitates the sensing, actuation, data capture, storage,processing, and/or analysis of data from a large array of connectedelements. These connected elements may be accessed remotely usingexisting network infrastructure to facilitate efficient and effectiveMachine-to-Machine (M2M) and Human to Machine (H2M) communication.During this communication, as the network of connected elements changesand collects data over time, a tremendous amount of data from theseconnected elements as well as external sources will be generated,stored, and facilitate correlations that have not been possible before.Issues organizing, making accessible, analyzing, operating, and/oracting on dynamic sets of connected elements are exacerbated by thedisparate heterogeneous nature of the underlying hardware andcorresponding associated data structures.

Data volume, variety, and velocity are all increasing at a rate wellbeyond the ability for most computational systems to effectively andefficiently organize and analyze past, present, and future data foraction using available methods. A present need exists for the ability tofacilitate the identification and analysis of data patterns, outliersand/or conditions which may relate to operational conditions within thetruly massive amount of data available online in today's connectedsystems. A further need exists to relate these data patterns, outliersand/or conditions to a point of actionable information regardingdiscrete connected elements within a specific system. It should beappreciated the disclosed method and system is not a mere presentationof available data, but a method and system which facilitates theorganization of data sets directed to the technical problem of searchingand correlating vast amounts of data to provide actionable informationto a system or user which through automation may be acted upon.

Since 2012, approximately 2.5 Exabytes (10¹⁸ bytes) of data are createdeach day. Most of this data is transactional or unstructured in nature.Examples include text, voice, video, and/or measurement data, such astemperature which may have neither set data structure nor associatedcontext. This volume of data being added to day after day is oftencalled the “data lake.”

Distributed residential, commercial, and/or industrial systems willcontain a variety of connected elements to provide data regarding aparticular facility. These connected elements may have differenthardware implementations that generate different values, units, orparameters to provide context to the data produced. Connected elementsmay also have characteristic data such as protocols, usage, physicalquantities, or topography relationships as well as contextual dataspecific to the application, such as data center, buildings, or smartgrid.

One of the key goals of an implementation of the system involvesfacilitating the identification and analysis of data patterns, outliersand/or conditions and correlating same to discrete connected elementswithin an infrastructure. This may be achieved through implementing atwo-part sequential federated query process: first querying atransactional-unstructured data source and second using the results fromthe first query for a second query into a contextual-characteristic datasource. More specifically, this first query expansively searches massivevolumes of data values stored in a transactional-unstructured datasource to identify operational anomalies within the data set. The secondquery involves processing the transactional-unstructured result data setand prepares to query a contextual-characteristic data source to providethe operational characteristics/context associated with the connectedelements identified within the transactional-unstructured result dataset. By conducting the second query, a system/user can validate anddetermine whether the data patterns, outliers and/or conditions areacceptable and within operational tolerances or symptomatic of animpending device failure.

Example applications of this method may include, but are not limited to:(1) managing a building HVAC system to assure the comfort of theoccupants, (2) maintenance of an office environmental air quality (whichmay include temperature, humidity, and carbon dioxide content) anddynamically adjusting an office environment according to the prevailingweather conditions, (3) management of a factory floor through monitoringand controlling day to day operations, maintenance, and oversight offacility operations. Commercial embodiments of such applications may beimplemented as a part of a home, building, or industrial automationsystem.

It is to be understood that the system described herein facilitatessignificant flexibility in terms of configuration, features,functionality, and/or end user application and although several examplesare described, a variety of alternative embodiment configurations andimplementations are possible.

FIG. 1 illustrates aspects of a system that facilitates generation ofand/or execution of a sequential federated query 100. The system forexecuting a sequential federated query method may include one or moreprocessing systems 110 and a cloud-computing environment 120. Connectedto a cloud-computing environment 120 are various building types such asresidential, commercial, and/or industrial buildings (140, 150, and 160respectively). Each building may have associated data storage arrays(130 a, 130 b, and 130 n respectively). One or more connected elements(shown in FIG. 2) are associated with these buildings, as are networkconnections 180 to allow the exchange of data between parts of thesystem.

There are no limitations implied to the type or number of buildingscomprising a system for a sequential federated query method 100.Embodiments for example, may include a residence 140 and an associateddata storage array 130 a, office buildings 150 and an associated datastorage array 130 b, or industrial installations 160 and an associateddata storage array 130 n. Each building may maintain a networkconnection 180 to the cloud-computing environment 120 and from theconnected elements in each building to each storage array via a networkconnection 180. It should be appreciated various parts of a system for asequential federated query method 100 facilitate co-located or remotestorage or processing solutions. For example, a data storage array 130 afor a residence 140 may be located within the residence 140 itself,outside yet nearby, in the cloud-computing environment 120, and/ordistributed across one or more storage nodes.

In one embodiment of the system illustrated in FIG. 1, the building 150contains one or more connected elements that perform sensing, actuation,data capture, storage, or processing for the monitoring or management ofthe building 150. Any variety of connected elements may be used tocapture, store, process data, actuate, and/or operate associated devicesover the network connections 180, to the cloud-computing environment120, to other parts of the system. These connected elements may includehardware, modules, and/or sensors.

For example, connected elements, sensors, or hardware may be configuredfor detecting temperature, humidity, ambient light, sound, smoke, carbonmonoxide, carbon dioxide, motion, non-conductive fluids, conductivefluids, vibration, energy, power, voltage, current, or any other desiredcharacteristic, and combination thereof. Connected elements may alsooperate, control, or articulate other connected elements, components,and/or other systems, such as turning on lights, opening a door orwindow, moving window shades, or triggering a door lock. Connectedelements may possess the ability to process data from other connectedelements or propagate data from one or more connected elements to one ormore other connected elements. Such hardware processing ability may bein addition to, or as a substitute for, measuring the environmentalparameters through a sensor. Any number of connected elements may bedeployed in any combination to monitor or manage a physical space,including for example a closet, room, residence, commercial building,campus, office, promenade, industrial setting, or any other desiredlocation.

Each building containing a connected element may ultimately connect to acloud-computing environment 120 through a network connection 180. Thisnetwork connection 180 allows access to the cloud-computing environment120 by a variety of devices capable of connecting to such an environmentin either a wired or wireless connection manner. From FIG. 1, suchdevices may include one or more processing systems 110 capable ofreceiving input from a user or to provide autonomous operation. One ormore associated data storage arrays 130 a, 130 b, 130 n may be utilizedto provide additional data storage capability ofcontextual-characteristic data, transactional-unstructured data, orboth. It should be appreciated a cloud computing environment 120, whileproviding additional communication paths to additional connectedelements or systems, is not required as part of the sequential federatedquery method. Embodiments contemplate self-contained, stand-alone, ordistributed systems.

FIG. 2 illustrates aspects of heterogeneous hardware connected elementsconnected to a system for executing a sequential federated query 200 inaccordance with various embodiments of this disclosure. In oneembodiment, the building 150 contains one or more types of connectedelements 210, 220, 230, 240 for the monitoring or management of thestructure. These connected elements 210, 220, 230, 240 communicate via awired network connection 250 or wireless network connection 260 networksand makes the data structures from each connected element available tothe cloud environment 120 via the network connections 180. The networkconnections 180 may include wired and/or wireless connection types.

For example, such connections may include, but are not limited to, anyphysical cabling method such as category 5 cable, coaxial, fiber,copper, twisted pair, or any other physical media to propagateelectrical signals. Wireless connections may include, but are notlimited to personal area networks (PAN), local area networks (LAN),Wi-Fi, Bluetooth, cellular, global, or space based communicationnetworks. Access between the cloud environment 120 and any other cloudenvironment is possible in other implementations these other cloudenvironments are configured to connect with devices similar to cloudenvironments such as the existing cloud environment 120. It is to beunderstood that the computing devices shown in the figures and discussedherein are intended to be illustrative only and that computing nodes andcloud computing environments may communicate with any type ofcomputerized device over any type of network with addressable or directconnections.

Any variety of connected elements may be used to perform organizing,making accessible, analysis, and operating or sensing, actuation, datacapture, storage, or processing over the network connection 180, to thecloud-computing environment 120, to other parts of the system.Accordingly, these devices may have different data parameters, fields,units, or general overall data structure associated with each device,respectively.

For example, as illustrated in FIG. 2, connected elements 210 may beconnected sensors to measure carbon dioxide for monitoring air qualityof the building 150 and communicate via a wired network connection 250.Connected elements may be both configured to acquire data and controlvarious modules for example a connected sensor to detect ambient lightand an actuator connected element 220 are implemented to change thestate of an occupant light fixture and communicate via a wired networkconnection 250. Connected elements may be connected sensors fortemperature and humidity connected element 230 to monitor environment ofthe building 150 and communicate via a wireless network connection 260.Finally, connected element 240 serves as a connected gateway tocommunicate with the associated connected elements 210, 220, 230, viatheir respective network connections 250, 260, process the datastructures of each, and transmit it to a network connection 180 fortransmission to the cloud environment 120. It should be appreciated acloud computing environment 120, while providing additionalcommunication paths to additional devices or systems, is not required aspart of the sequential federated query method. Other embodimentscontemplate self-contained, stand-alone systems, and/or distributedsystems.

These connected elements need not be geographically localized orlogically grouped in any way to utilize embodiments of this disclosure.Grouping connected elements geographically or logically may allow moreeconomic use. A geographic grouping such as in an apartment, home oroffice building may be accomplished, as well as logically locatingconnected elements by function. One of many logical grouping examplesmay be locating connected end points designed to sense temperature,proximate to an occupied location to detect changes in environment. Itshould be appreciated that the groupings of connected endpoints may alsobe located on a very large geographic scale, even globally. Such globaloperations may be monitored through a network located in any number offacilities around the globe.

FIG. 3 illustrates an exemplary deployment of heterogeneous hardwareconnected elements of a system 300 providing data for a system thatexecutes a sequential federated query in accordance with variousembodiments of this disclosure. A building 310 having (3) floors areillustrated. Floor (1) 312, Floor (2) 314, Floor (3) 316 are containedwithin the building 310. In FIG. 3, each floor has (3) connectedelements of different types. For example, connected elements may beconnected sensors to measure carbon dioxide 330, 332, 334 for monitoringair quality of the building 310 and communicate via a wired networkconnection. Connected elements may be both a connected sensor to detectambient light and an actuator 340, 342, 344 to change the state of anoccupant light fixture and communicate via a wired network connection.Connected elements may be connected sensors for temperature and humidity350, 352, 354 to monitor environment of the building 310 and communicatevia a wireless network connection.

Given the configuration illustrated in FIG. 3, each connected elementmay have a contextual-characterization data structure that includes, butnot be limited to, sensor specific information (temperature/humidity,carbon dioxide, and ambient light), geographic information (zone, floor,building), and network information (MAC address, IP address, wired,wireless). Other connected element information may be available as wellas information relative to the operation of the connected elementitself. As one example, a status of online or offline may be availableto further add to the data construct for each connected element.

Further, each connected element may have a transactional-unstructureddata structure that includes, but not limited to, sensor specificinformation (temperature/humidity values, carbon dioxide, and ambientlight in the present example), that is stored on a time, state, orunstructured basis. In this way, each connected element has a historyassociated with it. This history, or “data log”, may be used indetermining and identifying trends and/or operational characteristics intime data for particular connected elements. Further, data fromcombinations of history of various connected elements may be analyzedfor trends and/or operational characteristics for a particulargeographic space, system, and/or group of systems such as a building.Use of transactional-unstructured data is expected to grow in time andas such, a need for efficient query handling to allow the efficient andeffective querying of this ever expanding “data lake.”

Methods and systems that facilitate processing and executing federatedqueries for identifying and making accessible, actionable, andoperational data associated with or generated by residential,commercial, and/or industrial systems. Facilitating the identificationof operational anomalies and correlating these anomalies to discreteconnected elements within an infrastructure are realized throughexecuting a two part, sequential federated query. This sequentialfederated query is achieved through implementing a two-part sequentialfederated query process: first querying a transactional-unstructureddata source and second using the results from the first query to developa second query into a contextual-characteristic data source. Byconducting these queries in sequence, where the result from thetransactional-unstructured data source generated a second query into acontextual-characteristic data source, an efficient and effective methodof determining functional outliers is created.

FIG. 4A illustrates an exemplary data organization construct of a system400 providing data for systems that execute sequential federated queriesin accordance with various embodiments of this disclosure. Asillustrated in FIG. 4A, one or more processing systems 110 initiates asequential federated query through a network connection 180. The queryis executed in two parts. First, the system queries aTransactional-Unstructured Data Source (TUDS) 410. An example of a TUDSis described in FIG. 4B. Second, the results from the TUDS 410 queriesare processed and utilized to develop a query for aContextual-Characteristic Data Source (CCDS) 420. An example of a CCDSis described in FIG. 4C. Once processing on the data sources arecompleted, any actionable, operational data results and correspondingactivity is executed in the appropriate physical location, such as abuilding 310. As one example, determining what light sensors aremalfunctioning in a particular building of a campus utilizing lightsensor readings. These two steps streamline searching the massivevolumes of transactional-unstructured data available in the “data lake”by processing this TUDS result data set to identify data trends oranomalies using the CCDS to determine actionable or operational dataand/or solutions after providing connected element context are anefficient and effective method of determining, in the present example,functional outliers.

Both exemplary TUDS 410 and CCDS 420 are utilized sequentially in thedisclosed method of sequential federated queries to produce anactionable result. Embodiments of this disclosure contemplate this datamay be stored on a single data array, a plurality of data arrays, local,remote, cloud based, or any combination therein.

FIG. 4B illustrates an exemplary TUDS 410 organization construct ofconnected elements and associated data across a system that facilitatesa sequential federated query. Embodiments of a system for sequentialfederated queries exist where multiple connected elements are providingcontextual-characteristic data and transactional-unstructured data tothe system. It should be appreciated this data may be internal to aparticular system (such as a series of humidity measurements), externalto a system (such as power consumption rates provided by a utilitycompany), or both. Features of the system facilitate solving the issueof voluminous amounts of transactional-unstructured data when eachconnected element analyzed may have associatedtransactional-unstructured data as well as contextual-characteristicdata. It should be appreciated, both types of data or data sources arenot necessary for embodiments of this disclosure.

This TUDS 410 may include several data types as well. Time series data430 may contain several data fields that possess a Time Series ID, orsome unique identifier used as a key to identify devices in the CCDS420. Further, each time series data element may have a time and datestamp to identify the data packet to a particular moment in time, and/orunique device identifier. Finally, a parameter value may be associatedwith a time series data element to store one or more data values.Examples of time series data 430 may include measurements from atemperature sensor that over time may be graphed and plotted to show avisual curve.

Time series state data 440 is similar to time series data 430, howeverinstead of a numeric parameter stored from a sensor, a state may becaptured, such as “on” or “off” from a light sensor. An unlimited numberof states may be captured per sensor, or connected element such as“high”, “medium”, or “low” or any of the multiple states in aHierarchical State Machine (HSM). No limitations are implied with onetype of time series data from another.

Time stamp data 450 may also exist in transactional-unstructured datawhere otherwise unstructured data may have an associated time stamp. Anexample of this data type may be email messages that are time stamped onreceipt. Such transactional-unstructured data may be used in asequential federated query to assist in data correlations for a system.

Unstructured data 460 may exist which may not have an associated time,yet may be enormously useful in a sequential federated query to assistin data correlations, analysis, operations, and/or control for a system.An example of this data type may be data associated with social mediaapplications, images, text files, or other documents without a timestamp. Such transactional-unstructured data may be used in combinationwith contextual-characteristic data to form actionable correlations fora system. In other words, several heterogeneous data structures groupedtogether despite data structure inconsistencies. Such data exists in awide variety of formats and may reside both in transactional andnon-transactional type systems. In general, these types of data refer toinformation that may not have a defined data model or are organized in adefined manner at the time the data is created.

FIG. 4C illustrates exemplary CCDS 420 organization data construct forconnected elements associated with a system. It should be appreciatedthat any connected element type in any combination may exist in anygeographic location and include additional information within arespective data structure. Exemplary CCDS 420 data organization includeparameters such as protocols, usage, physical quantities, or topographyrelationships as well as ontologies specific and/or contextual to theapplication, connected element, or to the location, such as data center,buildings, or smart grid.

It should be appreciated that while each connected element may have anassociated contextual-characteristic data and transactional-unstructureddata structure, the number of data structures connected elements mayvary based on the hardware involved, the particular configuration, orapplication. Once the connected elements data structures are organizedin this way, multi-dimensional sequential federated analysis may beperformed without discrete or in depth knowledge of the physical systemand the associated connected elements. Further, the foregoing are onlyexamples of data and should not be considered limiting in any way.

FIG. 5 illustrates a block diagram of system components that organize,make accessible, analyze, operate, and execute a sequential federatedquery 500 in accordance with various embodiments of this disclosure. Itis possible for either a user and/or an automated process from a machineto generate and submit a sequential federated query. A user orsystem-initiated query may begin at a processing system 110. In otherimplementations, machine-initiated queries are necessarily derived fromany other process in the system. It should be appreciated the methods ofinitiation of a sequential federated query are not mutually exclusivefrom each other. In both cases, the sequential federated query processedby the Federated Query Handler 510, and is a structured federated querywith a particular grammar. In one example, SQL or any othertransactional-unstructured query language may be utilized to provide thestructured query grammar. This grammar structure may include the use ofvarious data sources, operations such as matching or graphing,assignments, aggregating, or sub queries.

Sequential federated queries are received into the Federated QueryHandler 510, illustrated in FIG. 5 as a series of modules including: AQuery Deconstruction (QD) function module 520, a Data SourceIdentification and Processing (DSIP) function module 530, and a ResultTransformation and Filtering (RTF) function module 540. Additionally, aTransactional-Unstructured Data Source (TUDS) 410 may store measurement,parametric, textual, time stamp, or other data associated with connectedelements, or other data not associated with any connect elements. Anexample of this data set may include all electrical rate data for agiven power producing facility. Further, a Contextual-CharacteristicData Source (CCDS) 420 which may store contextual-characteristic dataassociated with connected elements in a structure, such as a building310 is also present. It should be appreciated both data sources mayreside on the same physical device, or across one or multiple systems.

The Query Decoder (QD) 520 analyzes the sequential federated query anddeconstructs it into query elements. These query elements may includecontextual data or operators, such as the location of any data sourcesto be used, operational parameters for the data, filtering to beperformed on a result, and any output format.

Query elements are processed and utilized by the Data SourceIdentification and Processing (DSIP) function module 530, to analyze thequery elements and perform operations on the translated data sources orquery built from the resulting data based on the deconstructed queryelements. These data sources may include TUDS 410, CCDS 420, and/or acombination of data sources. In an example described herein, the TUDS410 is queried first, the results processed, translated, and utilized toperform the query on the CCDS 420. Due to the divergent data storageparadigms of the TUDS and CCDS, it is necessary to process the queryaccording to the target data source paradigm. DSIP is capable ofgenerating a query in accordance with the targeted storage datatechnology. Data storage technology exposes a query language such asSPARQL, SQL, MongoDB query, and others.

A TUDS query will be executed by the Data Source Identification andProcessing (DSIP) function module 530, and sent 550 to the TUDS 410.Once the query is complete the TUDS result data will be returned 555 tothe Data Source Identification and Processing (DSIP) function module530. Similarly, a CCDS query developed based on a TUDS query data resultreturned 555 will be executed by the Data Source Identification andProcessing (DSIP) function module 530, and sent 560 to the CCDS 420.Once the query is complete the CCDS result data will be returned 565 tothe Data Source Identification and Processing (DSIP) function module530.

Once the data has been queried in the TUDS 410, and CCDS 420, resultsdefined by the query elements are processed and/or filtered andtranslated into a format specified in the initial sequential federatedquery by the Results Transformation and Filtering module 540. Dataformat examples prepared for translation may include CSV, XML, JSON, orRDF. Translated results are transmitted back to the processing system110 for the operation or action in the respective environment, such as abuilding management system in execution within a building 310.

FIG. 6A and FIG. 6B are flow diagrams illustrating methods of executinga sequential federated query 600 in accordance with various embodimentsof this disclosure. As discussed, a sequential federated query isreceived 610 from a user or processing system. This may be a manualaction from a user, an automated action from another processing system,or some combination of both. It should be appreciated that more than onedata source or connected elements may be the target of the sequentialfederated query. This includes both internal system and external datasuch as from a “data warehouse” or “data lake.” It should be appreciatedthe disclosed method does not result in a mere presentation of availabledata, but a method which facilitates the organization of data setsdirected to the technical problem of searching and correlating vastamounts of data to provide actionable information to a system or userwhich through automation may be acted upon.

Once received, the sequential federated query is deconstructed into itscomposite query elements 620. These query elements may include thelocation of any data sources to be used, operational parameters for thedata, as well as any filtering and/or processing to be performed on aresult. Once deconstructed, error checking is performed 625 to determinewhat sequential federated query and corresponding data source type isbeing requested, and if such a request can be met based on thedetermined query elements. If the query elements determined are notcorrect to support the type of query requested, the process returns toreceiving the sequential federated query 610 input elements forresubmission and/or restructuring of the sequential federated query.

If the query elements are correct to support the type of queryrequested, the method continues by identifying thetransactional-unstructured data source (TUDS), contextual-characteristicdata source (CCDS), and/or any data organization parameters 630.Validation 635 is also performed to verify, in one example, if the datasources currently exist in the form or location specified in thesequential federated query. Further, a determination may be made as tothe types of available data and/or if the sources are appropriate forsuch a query. For example, further logic may be required if a particulartype of contextual-characteristic is present, or if multiple sources oftransactional-unstructured exist.

Once each data source is validated 635, the transactional-unstructureddata set is queried based on the data organizational parameters 640determined from the sequential federated query. This query on thetransactional-unstructured data creates a focused TUDS result data setin a format such as time series data 430, time series state data 440,time stamp data, 450 or unstructured data 460. This TUDS result data setmay then be processed to develop a TUDS result data set to obtaincontext for the TUDA data. This produces a produce a refined data setmost useful to a system user, such as a building administrator, and/or aparticular system in a time efficient way. This flow continues from FIG.6A to FIG. 6B 645.

Once the transactional-unstructured query creates a focused data set,this data set is transformed into a contextual-characteristic sourcequery 650. Formats for this data set may include any semantic weblanguage designed to represent relationships between connected elements.Examples may include, but are not limited to, RDF, OWL, JSONLD, JSON,XML, or CSV. It should be appreciated the order in which the datasources are queried is an important aspect to achieve the efficienciesdescribed herein. This sequential data source ordered approach of (1)transactional-unstructured source query, followed by (2)contextual-characteristic source query, facilitates the efficient searchof an expansive TUDS and contextual-characteristic data set bysubstantially focusing the step (2) data query to identifycontextual-characteristic data that is ultimately associated withtransactional-unstructured data in step (1).

From the query results from the contextual-characteristic data set, aresult data set is extracted based on the results from the data sourcesand the data organization parameters 660. From this result data set, arefined data set will be created 670 in a contextual-characteristicformat such as, OWL, JSONLD, JSON, XML, or CSV. This result data set isthen processed and/or filtered based on the query parameters andtransformed to form a final data set 680. Data format examples preparedfor translation may include CSV, XML, JSON, or RDF.

This final data set is then validated 685 to determine what actions,operations, or analysis may be taken based on the result data. If theseactions are determined to be valid, they may be transmitted to a user ora management system, such as a building management system for execution.If these actions are determined not to be valid, processing returns toreceiving sequential federated queries 695 for resubmission.

FIG. 7 illustrates an exemplary system for data organization andprocessing flow diagram 700 for executing a sequential federated queryin accordance with various embodiments of this disclosure. Embodimentsof this system illustrate a sequential federated query received uponinput from a user or processing system. Creation of the sequentialfederated query may be a manual action from a user, generated as part ofor from another processing system, or some combination of both 701. Asequential federated query may utilize languages such as SQL, OData,MongoDB, SPARQL, or any other to query any transactional-unstructureddata source 751, illustrated in FIG. 7 as “System 1”.

Once received, the sequential federated query is deconstructed into itscomposite query elements 702. These query elements may include thelocation of any data sources to be used, such as theTransactional-Unstructured Data Source (TUDS) within “System 1” 751 orContextual-Characteristic Data Source (CCDS) within “System 2” 752.Operational parameters for the data, as well as any filtering to beperformed on a result may also be included in the query elements.

A query is performed 703 on the TUDS of “System 1” 751 based on thequery elements deconstructed from the initial sequential federated query702. This transactional-unstructured data based results from the TUDSquery performed on “System 1” are received 704 by the system, processed705, and translated into a contextual-characteristic data format 706such as OWL, JSONLD, JSON, XML, or CSV, or others.

Now translated in a contextual-characteristic data format 706, theseresults are injected as a query 707 into the CCDS of “System 2” 752.This query may be formatted as SPARQL or any other query language. Thisquery is processed 708 by “System 2” 752 and the result 709 yields datain a contextual-characteristic data format, yet with reference to thepreviously specified transactional-unstructured results data only. It ishere that one of the efficient and effective data processing gains arerealized through this two part, sequential federated query process.Executed in this sequential order, the system facilitates the processingand execution of sequential federated queries for identifying and makingaccessible, actionable, and operational data associated with orgenerated by residential, commercial, and/or industrial systems in anefficient and effective way.

Results from the query into the CCDS of “System 2” 752 are returned tothe system and processed and/or filtered 710 according to the initialsequential federated query parameters. Finally, the results aretranslated into the format requested 711, which may include CSV, XML,JSON, RDF. This format may be used by human and/or machine as a methodto generate and/or execute actions derived from the resultant data.

FIG. 8 illustrates a working example 800 of the processing steps fromFIG. 7 implementing an industrial building facility and energymanagement system. An industrial building 810 having three distinctbuilding sections illustrated including: a machine shop 820, HVAC Room830, and Office Space 840. Each distinct building section has threeconnected elements of different types, including connected sensors tomeasure carbon dioxide sensor connected elements 850, 852, 854 andconnected sensors to detect occupancy/actuate for lighting connectedelements 860, 862, 864. Connected elements may be connected sensors forpower measurement 870, 872, 874 to monitor energy consumption of thesections of the building 810. Such a system also has a CCDS 880 tocollect contextual-characteristic data specifically about the building810 and the connected elements contained therein. The CCDS 880 may becontained within the building 810, be in a remote location, and/or cloudbased. Further, a TUDS 890 that may be in a remote location or cloudbased is also connected to the system through a network connection toprovide transactional-unstructured data to the system including forexample thirty days of electrical data captured periodically at 60 Hz.

As an example, a sequential federated query may be executed to determinewhich, if any, connected elements have become inoperable or areotherwise malfunctioning in the building 810. In such a sequentialfederated query, a user or system may query all data stored in the TUDS890 for operational conditions and/or anomalies, such as an offlinestatus or given length of time. Other data in the TUDS 890 may be usedas well such as measurement, time stamp, ID, status, state, and/or otherdata that may be captured or related from another data source. It shouldbe appreciated there is no contextual relation to the individualconnected element nor building 810 itself in this TUDS 890 data record.Any context or characteristic regarding the particular related connectedelement is achieved in the second step of the sequential federated querywhen the TUDS result data set is injected into the CCDS 880 to relatethe TUDS result data to particular connected elements.

A historical comparison of the TUDS result data set may indicate whatconnected elements were, for example, previously operating properly asdefined by online status or discrete temperature/humidity measurementson the periodic basis. For example, if there is current TUDS data whichcontains a state of “offline” or a temperature/humidity sensor withinthe building 810 which is reporting 1000 degrees Celsius at 300%relative humidity (clearly erroneous measurements), but has previouslyreported being in an “online” status and measurement data more likelyfor an indoor environment, such data may be part of the TUDS result dataset to determine, in this example, functional or operational outliers.The TUDS result data set which contains such functional outliers is thenprocessed, developed as a query, and then injected into the CCDS 880 toprovide specific context regarding the discrete connected elements tothe building 810. This results in a CCDS result data set that may beacted upon by a user and/or system.

If for example, a sequential federated query is run for the building 810for malfunctioning sensors and the query of the TUDS 890 determines aseries of data with particular IDs which contain a status of “offline”as well as associated measurement data, time stamp, ID, or any otherassociated data, these data records may become part of the TUDS resultdata set. In one example, there may be three data records that may eachindicate a unique ID and functional status, in this case, “offline” Foran extended length of time. This TUDS result data set is then processedand injected as a query into the CCDS 880 to provide context as to whatdiscrete sensors are at issue, and additional context about same.

In this example, a CCDS result data set may indicate that specificconnected elements 854, 864, 874 of the “Office Space “are reporting astatus of “offline.” This further contextual information would provideinformation that each of these sensors are located in the Office Space840 of the building 810. Further, each connected element of differenttypes, including connected sensors to measure carbon dioxide 854,occupancy/actuate 864, and power measurement 874 would also haveassociated contextual-characterization information related to eachdiscrete connected element.

With this total understanding of the TUDS identified outlier data andassociated characteristics of the particular sensors, an operation canbe undertaken to repair these individual connected elements. This typeof sequential federated query allows a maintenance operator to determineissues and subsequent actions in a timely fashion for a very large andheterogeneous distribution of connected sensors and act on themaccordingly.

As a direct result of this querying and processing of the TUDS 890 andCCDS 880 in this sequence, a system or user can derive a survey of anenvironment to efficiently and effectively query huge amounts of dataand facilitate the identification and analysis of data patterns,outliers and/or conditions which may relate to operational conditions. Afurther ability exists utilizing this method to relate these datapatterns, outliers and/or conditions to a point of actionableinformation regarding discrete connected elements within a specificsystem.

It should be appreciated there are many examples of use of data derivedfrom a TUDS 890 and injected into a CCDS 880. While determination offunctional outliers is but one example, combination with other TUDSexternal to an infrastructure being monitored is possible. In this way,the combination of various TUDS sources may be used to form advancedanalytics and have the result relate back to particular infrastructureor systems being monitored.

In this way manual, autonomous, and/or control actions may be executedgiven one or more environmental considerations as determined bycontextual-characteristic data type data. Such environmentalconsiderations are monitored through heterogeneous connected elementsand correlated with large volumes of transactional-unstructured data.These described systems and methods facilitate processing and executingsequential federated queries for identifying and making accessible,actionable, and operational data associated with or generated byresidential, commercial, and/or industrial systems. Efficient andeffective data processing gains are realized through this two part,sequential federated query process.

It should be appreciated the disclosed method and system is not a merepresentation of available data, but a method and system whichfacilitates the organization of data sets directed to the technicalproblem of searching and correlating vast amounts of data to provideactionable information to a system or user which through automation maybe acted upon.

Any processing systems used in various embodiments of this disclosuremay be, for example, processing systems such as those based on IntelPENTIUM-type processor, Motorola PowerPC, Sun UltraSPARC,Hewlett-Packard PA-RISC processors, or any other type of processor.

For example, various embodiments of the disclosure may be implemented asspecialized software executing in a processing system 900 such as thatshown in FIG. 9. The processing system 900 may include a processor 920connected to one or more memory devices 930, such as a disk drive,memory, or other device for storing data. Memory 930 is typically usedfor storing programs and data during operation of the processing system900. The processing system 900 may also include a storage system 950that provides additional storage capacity. Components of processingsystem 900 may be coupled by an interconnection mechanism 940, which mayinclude one or more busses (e.g., between components that are integratedwithin the same machine) and/or a network (e.g., between components thatreside on separate discrete machines). The interconnection mechanism 940enables communications (e.g., data, instructions) to be exchangedbetween system components of system 900.

Processing system 900 also includes one or more input devices 910, forexample, a keyboard, mouse, trackball, microphone, touch screen, and oneor more output devices 960, for example, a printing device, displayscreen, speaker. In addition, processing system 900 may contain one ormore interfaces (not shown) that connect processing system 900 to acommunication network (in addition or as an alternative to theinterconnection mechanism 940).

The storage system 950, shown in greater detail in FIG. 10, typicallyincludes a computer readable and writeable nonvolatile recording medium1010 in which signals are stored that define a program to be executed bythe processor or information stored on or in the medium 1010 to beprocessed by the program to perform one or more functions associatedwith embodiments described herein. The medium may, for example, be adisk or flash memory. Typically, in operation, the processor causes datato be read from the nonvolatile recording medium 1010 into anothermemory 1020 that allows for faster access to the information by theprocessor than does the medium 1010. This memory 1020 is typically avolatile, random access memory such as a dynamic random access memory(DRAM) or static memory (SRAM). It may be located in storage system1000, as shown, or in memory system 930. The processor 920 generallymanipulates the data within the integrated circuit memory 930, 1020 andthen copies the data to the medium 1010 after processing is completed. Avariety of mechanisms are known for managing data movement between themedium 1010 and the integrated circuit memory element 930, 1020, and thedisclosure is not limited thereto. The disclosure is not limited to aparticular memory system 930 or storage system 950.

The processing system may include specially programmed, special-purposehardware, for example, an application-specific integrated circuit(ASIC). Aspects of the disclosure may be implemented in software,hardware or firmware, or any combination thereof. Further, such methods,acts, systems, system elements and components thereof may be implementedas part of the processing system described above or as an independentcomponent.

Although processing system 900 is shown by way of example as one type ofprocessing system upon which various aspects of the disclosure may bepracticed, it should be appreciated that aspects of the disclosure arenot limited to being implemented on the processing system as shown inFIG. 10. Various aspects of the disclosure may be practiced on one ormore computers having a different architecture or components shown inFIG. 10. Further, where functions or processes of embodiments of thedisclosure are described herein (or in the claims) as being performed ona processor or controller, such description is intended to includesystems that use more than one processor or controller to perform thefunctions.

Processing system 900 may be a processing system that is programmableusing a high-level computer programming language. Processing system 900may be also implemented using specially programmed, special purposehardware. In processing system 900, processor 920 is typically acommercially available processor such as the well-known Pentium classprocessor available from the Intel Corporation. Many other processorsare available. Such a processor usually executes an operating systemwhich may be, for example, the Windows 95, Windows 98, Windows NT,Windows 2000, Windows ME, Windows XP, Vista, Windows 7, Windows 10, orprogeny operating systems available from the Microsoft Corporation, MACOS System X, or progeny operating system available from Apple Computer,the Solaris operating system available from Sun Microsystems, UNIX,Linux (any distribution), or progeny operating systems available fromvarious sources. Many other operating systems may be used.

The processor and operating system together define a computer platformfor which application programs in high-level programming languages arewritten. It should be understood that embodiments of the disclosure arenot limited to a particular processing system platform, processor,operating system, or network. In addition, it should be apparent tothose skilled in the art that the present disclosure is not limited to aspecific programming language or processing system. Further, it shouldbe appreciated that other appropriate programming languages and otherappropriate processing systems could also be used.

One or more portions of the processing system may be distributed acrossone or more processing systems coupled to a communications network. Forexample, as discussed above, a processing system that determinesavailable power capacity may be located remotely from a system manager.These processing systems also may be processing system systems. Forexample, various aspects of the disclosure may be distributed among oneor more processing systems configured to provide a service (e.g.,servers) to one or more client computers, or to perform an overall taskas part of a distributed system. For example, various aspects of thedisclosure may be performed on a client-server or multi-tier system thatincludes components distributed among one or more server systems thatperform various functions according to various embodiments of thedisclosure. These components may be executable, intermediate (e.g., IL)or interpreted (e.g., Java) code which communicate over a communicationnetwork (e.g., the Internet) using a communication protocol (e.g.,TCP/IP). For example, one or more database servers may be used to storedevice data, such as expected power draw, that is used in designinglayouts associated with embodiments of the present disclosure.

It should be appreciated that the disclosure is not limited to executingon any particular system or group of systems. In addition, it should beappreciated that the disclosure is not limited to any particulardistributed architecture, network, or communication protocol.

Various embodiments of the present disclosure may be programmed using anobject-oriented programming language, such as SmallTalk, Java, C++, Ada,or C# (C-Sharp). Other object-oriented programming languages may also beused. Alternatively, functional, scripting, and/or logical programminglanguages may be used, such as BASIC, ForTran, COBoL, TCL, or Lua.Various aspects of the disclosure may be implemented in a non-programmedenvironment (e.g., documents created in HTML, XML or other format that,when viewed in a window of a browser program render aspects of agraphical-user interface (GUI) or perform other functions). Variousaspects of the disclosure may be implemented as programmed ornon-programmed elements, or any combination thereof.

Embodiments of a systems and methods described above are generallydescribed for use in relatively large data centers having numerousequipment racks; however, embodiments of the disclosure may also be usedwith smaller data centers and with facilities other than data centers.Some embodiments may also be a very small number of computersdistributed geographically to not resemble a particular architecture.

In embodiments of the present disclosure discussed above, results of theanalysis are described as being provided in real-time. As understood bythose skilled in the art, the use of the term real-time is not meant tosuggest that the results are available immediately, but rather, areavailable quickly giving a designer the ability to try a number ofdifferent designs over a short period of time, such as a matter ofminutes.

Having thus described several aspects of at least one embodiment of thisdisclosure, it is to be appreciated various alterations, modifications,and improvements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the spirit and scope ofthe disclosure. Accordingly, the foregoing description and drawings areby way of example only.

The invention claimed is:
 1. A method of processing a sequentialfederated query for distributed systems, comprising: receiving, at aprocessor, a sequential federated query; deconstructing, at theprocessor, the sequential federated query into query elements;identifying, at the processor, a first data source, a second datasource, and a data organization parameter based on the query elements,wherein data stored in the first data source includes unstructured dataand data structured according to several heterogeneous data structures;generating, at the processor, a first query based on the dataorganization parameter; providing, by the processor, the first query tothe first data source; generating, at the processor, a first result dataset from the first data source based on a response to the first queryreceived from the first data source; processing, at the processor, thefirst result data set and the data organization parameter to develop asecond query; generating, at the processor, a second result data setfrom the second data source based on the developed second query and thedata organization parameter; generating, at the processor, a finalsequential federated query data set based on the second result data setand the data organization parameter; processing, at the processor, aformatted sequential federated query data set based on the processing ofthe final sequential federated query data set and the data organizationparameter; and providing, at the processor, the formatted sequentialfederated lath set to a management system for action.
 2. The method ofclaim 1, wherein receiving the sequential federated query is initiatedfrom one of a user and a system.
 3. The method of claim 1, whereinreceiving the sequential federated query is from one of a database, auser interface, and an application interface.
 4. The method of claim 1,wherein the query elements are time series based.
 5. The method of claim4, wherein the query elements are one of time series data, time seriesstate data, time stamp data, and unstructured data formats.
 6. Themethod of claim 1, wherein one of a plurality of the second data source,the first data source, and data organization parameter based on thequery elements are utilized.
 7. The method of claim 1 wherein, the firstresult data set includes operational anomaly data generated by connectedelements.
 8. The method of claim 1, wherein the second result data setis contextual-based data.
 9. The method of claim 8, wherein thecontextual based data is one of data locations, data operations, anddata sources.
 10. The method of claim 1, wherein the management systemfor action is a Building Management System (BMS).
 11. A non-transitorycomputer readable medium storing sequences of computer-executableinstructions for processing a sequential federated query for distributedsystems, the sequences of computer executable instructions includinginstructions that instruct at least one processor to: receive asequential federated query; deconstruct the sequential federated queryinto query elements; identify a first data source, a second data source,and a data organization parameter based on the query elements, whereindata stored in the first data source includes unstructured data and datastructured according to several heterogeneous data structures; generatea first query based on the data organization parameter; provide thefirst query to the first data source; generate a first result data setfrom the first data source based on a response to the first queryreceived from the first data source; process the first result data setand the data organization parameter to develop a second query; generatea second result data set from the second data source based on thedeveloped second query and the data organization parameter; generate afinal sequential federated query data set based on the second resultdata set and the data organization parameter; process a formattedsequential federated query data set based on the processing of the finalsequential federated query data set and the data organization parameter;and provide, at the processor, the formatted sequential federated querydata set to a management system for action.
 12. The non-transitorycomputer readable medium of claim 11, wherein the at least one processoris further configured to receive the sequential federated query from oneof a user and a system.
 13. The non-transitory computer readable mediumof claim 11, wherein the at least one processor is further configured toreceive the sequential federated query from one of a database, a userinterface, and an application interface.
 14. The non-transitory computerreadable medium of claim 11, wherein the query elements are time seriesbased.
 15. The non-transitory computer readable medium of claim 14,wherein the at least one processor is further configured that at leastone of the query elements are one of time series data, time series statedata, time stamp data, and unstructured data formats.
 16. Thenon-transitory computer readable medium of claim 11, wherein a pluralityof the second data source, the first data source, or data organizationparameter based on the query elements are utilized.
 17. Thenon-transitory computer readable medium of claim 11, wherein the firstresult data set includes operational anomaly data generated by connectedelements.
 18. The non-transitory computer readable medium of claim 11,wherein the second result data set is contextual based data.
 19. Thenon-transitory computer readable medium of claim 18, wherein the atleast one processor is further configured that the contextual based dataare one of data locations, data operations, and data sources.
 20. Thenon-transitory computer readable medium of claim 11, wherein the atleast one processor is further configured that the management system foraction is a Building Management System (BMS).